A computer vision approach for quantifying leaf shape of maize (Zea mays L.) and simulating its impact on light...

Area of Science:

• Plant morphology and biophysics
• Agricultural engineering and remote sensing
• Computational biology and crop modeling

Background:

• Accurate leaf shape quantification is vital for plant modeling and understanding light interception.
• Traditional manual leaf measurements are laborious, error-prone, and challenging for large, undulating maize leaves.

Purpose of the Study:

• To develop and validate a novel, image-based computer vision method for precise maize leaf shape analysis.
• To overcome limitations of manual measurements in determining leaf shape parameters for functional-structural plant models (FSPMs).

Main Methods:

• A new camera system (GoPro Hero8 Black integrated with LI-3100C Area Meter) captured high-resolution leaf videos.
• Semi-automated software was developed for object detection, contour extraction, and leaf width determination.
• Validation involved pixel-counting, contrast analysis, and comparison with standard manual measurements.

Main Results:

• The camera method demonstrated accuracy and reliability in determining leaf shape parameters.
• Significant differences (p < 0.01) in leaf shape parameters (alpha, a) were found between maize cultivars and leaf ranks.
• Simulations showed leaf shape variations can impact light interception by up to 7%.

Conclusions:

• The developed camera method offers an accurate and efficient alternative to manual measurements for maize leaf shape analysis.
• Precise leaf shape data is crucial for improving the parameterization of crop growth models and FSPMs.
• This approach facilitates future research on rank-dependent leaf shape effects, enhancing canopy representation and agricultural decision-making.